Artikler med mandater om offentlig tilgang - Masashi SugiyamaLes mer
Ikke tilgjengelig noe sted: 1
Learning Intention-Aware Policies in Deep Reinforcement Learning
T Zhao, S Wu, G Li, Y Chen, G Niu, M Sugiyama
Neural Computation 35 (10), 1657-1677, 2023
Mandater: National Natural Science Foundation of China
Nektet opplastet: 2
Centroid estimation with guaranteed efficiency: A general framework for weakly supervised learning
C Gong, J Yang, J You, M Sugiyama
IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (6), 2841-2855, 2020
Mandater: National Natural Science Foundation of China
Ansvarlige forfattere: C Gong
Class-wise denoising for robust learning under label noise
C Gong, Y Ding, B Han, G Niu, J Yang, J You, D Tao, M Sugiyama
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 2835-2848, 2022
Mandater: National Natural Science Foundation of China
Ansvarlige forfattere: C Gong
Tilgjengelige et eller annet sted: 76
Co-teaching: Robust training of deep neural networks with extremely noisy labels
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama
Advances in neural information processing systems 31, 2018
Mandater: Research Foundation (Flanders), National Natural Science Foundation of China
How does disagreement help generalization against label corruption?
X Yu, B Han, J Yao, G Niu, I Tsang, M Sugiyama
International conference on machine learning, 7164-7173, 2019
Mandater: Research Foundation (Flanders)
Attacks which do not kill training make adversarial learning stronger
J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli
International conference on machine learning, 11278-11287, 2020
Mandater: National Natural Science Foundation of China, Research Grants Council, Hong …
Are anchor points really indispensable in label-noise learning?
X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama
Advances in neural information processing systems 32, 2019
Mandater: Australian Research Council, National Natural Science Foundation of China
Part-dependent label noise: Towards instance-dependent label noise
X Xia, T Liu, B Han, N Wang, M Gong, H Liu, G Niu, D Tao, M Sugiyama
Advances in Neural Information Processing Systems 33, 7597-7610, 2020
Mandater: Australian Research Council, National Natural Science Foundation of China
Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
A Cichocki, AH Phan, Q Zhao, N Lee, I Oseledets, M Sugiyama, ...
Foundations and Trends® in Machine Learning 9 (6), 431-673, 2017
Mandater: UK Engineering and Physical Sciences Research Council
Masking: A new perspective of noisy supervision
B Han, J Yao, G Niu, M Zhou, I Tsang, Y Zhang, M Sugiyama
Advances in neural information processing systems 31, 2018
Mandater: US National Science Foundation, Research Foundation (Flanders), National …
Dual t: Reducing estimation error for transition matrix in label-noise learning
Y Yao, T Liu, B Han, M Gong, J Deng, G Niu, M Sugiyama
Advances in neural information processing systems 33, 7260-7271, 2020
Mandater: Australian Research Council, National Natural Science Foundation of China
Progressive identification of true labels for partial-label learning
J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama
international conference on machine learning, 6500-6510, 2020
Mandater: National Natural Science Foundation of China
Provably consistent partial-label learning
L Feng, J Lv, B Han, M Xu, G Niu, X Geng, B An, M Sugiyama
Advances in neural information processing systems 33, 10948-10960, 2020
Mandater: National Natural Science Foundation of China, National Research Foundation …
Sigua: Forgetting may make learning with noisy labels more robust
B Han, G Niu, X Yu, Q Yao, M Xu, I Tsang, M Sugiyama
International Conference on Machine Learning, 4006-4016, 2020
Mandater: Australian Research Council, Research Foundation (Flanders), Research Grants …
Provably end-to-end label-noise learning without anchor points
X Li, T Liu, B Han, G Niu, M Sugiyama
International conference on machine learning, 6403-6413, 2021
Mandater: Australian Research Council, National Natural Science Foundation of China
Confidence scores make instance-dependent label-noise learning possible
A Berthon, B Han, G Niu, T Liu, M Sugiyama
International conference on machine learning, 825-836, 2021
Mandater: Australian Research Council, National Natural Science Foundation of China
Learning with multiple complementary labels
L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama
International conference on machine learning, 3072-3081, 2020
Mandater: Research Grants Council, Hong Kong, National Research Foundation, Singapore
In Search of Non-Gaussian Components of a High-Dimensional Distribution.
G Blanchard, M Kawanabe, M Sugiyama, V Spokoiny, KR Müller, ...
Journal of Machine Learning Research 7 (2), 2006
Mandater: German Research Foundation
Instance-dependent label-noise learning with manifold-regularized transition matrix estimation
D Cheng, T Liu, Y Ning, N Wang, B Han, G Niu, X Gao, M Sugiyama
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Mandater: Australian Research Council, National Natural Science Foundation of China
Maximum mean discrepancy test is aware of adversarial attacks
R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama
International Conference on Machine Learning, 3564-3575, 2021
Mandater: Australian Research Council, National Natural Science Foundation of China
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